Home›Compute
Compute
Compute
The chips that train and run AI models: GPUs, TPUs and other accelerators, and which models were trained on them.
Total 6 · Clear filters
2023
-
Dec 6, 2023
AMD Instinct MI300X
AMD's data-center GPU for generative AI, released in December 2023. Built on the CDNA 3 architecture with a chiplet design, it leads with 192 GB of HBM3 memory, which AMD contrasted with the 80 GB on Nvidia's H100.
-
Mar 2023
NVIDIA H800
The H800 is a China-market version of NVIDIA's H100 data-center GPU, adapted to fit the US export rules of October 2022 by cutting chip-to-chip transfer speed. It was later used to train DeepSeek-V3 and was covered by tighter US controls in October 2023.
2022
-
Mar 22, 2022
NVIDIA H100
The NVIDIA H100 is the first data-center GPU built on NVIDIA's Hopper architecture, announced in March 2022 with 80 billion transistors, HBM3 memory, FP8 Tensor Cores and a Transformer Engine aimed at large language models.
2020
-
May 14, 2020
NVIDIA A100
NVIDIA's A100 is a data-center GPU, the first built on the Ampere architecture. Announced on 14 May 2020, it introduced third-generation Tensor Cores, the TF32 format and Multi-Instance GPU, and became a standard building block for training large language models.
2016
-
Apr 5, 2016
NVIDIA Tesla P100
The Tesla P100, announced by NVIDIA in April 2016, was the first data-center GPU on the Pascal architecture, and the first to combine stacked HBM2 memory, half-precision math aimed at deep learning and the NVLink interconnect.
—
-
Tenstorrent Blackhole
Blackhole is Tenstorrent's AI accelerator chip; Galaxy Blackhole is a server holding 32 of them, rated at 23 PFLOPS of block FP8, with Ethernet links wiring chips and clusters together.